Customer-obsessed science
Research areas
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July 9, 202610 min readA new Rust proxy called Turnstile sits between the model backend and the agent harness to capture information lost in mere text transcripts. -
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Featured news
Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features researchers tackling the hardest problems in agentic AI — from building reliable perception systems to designing training environments that mirror human learning.
The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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ECOOP 20232023Reliable storage systems must be crash consistent – guaranteed to recover to a consistent state after a crash. Crash consistency is non-trivial as it requires maintaining complex invariants about persistent data structures in the presence of caching, reordering, and system failures. Current programming models offer little support for implementing crash consistency, forcing storage system developers to roll
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KDD 2023 International Workshop on Mining and Learning from Time Series (MileTS)2023Ensembles have long been a cornerstone in improving performance by integrating black-box base learners. The primary approach, “stacked generalization” is inherently static, lacking adaptability to changes in input data post-training. This limitation is more pronounced in time series forecasting, where these methods struggle to manage temporal correlations or non-stationarity. Given base learners may perform
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ICML 2023 Workshop on Interactive Learning with Implicit Human Feedback2023In this work, we show how to collect and use human feedback to improve complex models in information retrieval systems. Human feedback often improves model performance, yet little has been shown to combine human feedback and model tuning in an end-to-end setup with public resources. To this end, we develop a system called Crowd-Coachable Retriever (CCR),1 where we use crowd-sourced workers and open-source
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KDD 2023 International Workshop on Mining and Learning from Time Series (MileTS)2023Predictive maintenance (PdM) is one of the most important machine learning (ML) use cases in a wide range of businesses that own or manufacture machinery. Many PdM applications utilize event logs/sequences that record machine operating conditions and health data. However, industrial scale PdM use cases bring a variety of challenges which makes it difficult to directly apply state-of-the-art models. In this
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KDD 2023 Workshop on Online and Adaptive Recommender Systems (OARS)2023Rogue actors employ sophisticated automation techniques to mimic human browsing/click patterns and generate invalid (i.e., fraudulent or robotic) traffic on retail marketplaces to artificially inflate their key performance metrics at the expense of their legitimate competitors. To maintain a clean and fair advertising system, it is essential to identify and mitigate ad traffic that is invalid, i.e., fraudulent
Collaborations
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Gretchen Ertl
The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.
A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
Credit: Wolfram Scheible
We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
Courtesy of Pai-Ling Yin
We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
